Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations24218
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 MiB
Average record size in memory297.0 B

Variable types

Numeric20
Categorical8

Alerts

blueTeamDragonKills is highly overall correlated with redTeamDragonKillsHigh correlation
blueTeamInhibitorsDestroyed is highly overall correlated with blueTeamTowersDestroyedHigh correlation
blueTeamMinionsKilled is highly overall correlated with blueTeamXpHigh correlation
blueTeamTotalDamageToChamps is highly overall correlated with blueTeamTotalGold and 1 other fieldsHigh correlation
blueTeamTotalGold is highly overall correlated with blueTeamTotalDamageToChamps and 4 other fieldsHigh correlation
blueTeamTotalKills is highly overall correlated with blueTeamTotalDamageToChamps and 2 other fieldsHigh correlation
blueTeamTowersDestroyed is highly overall correlated with blueTeamInhibitorsDestroyed and 2 other fieldsHigh correlation
blueTeamTurretPlatesDestroyed is highly overall correlated with redTeamTotalGold and 1 other fieldsHigh correlation
blueTeamXp is highly overall correlated with blueTeamMinionsKilled and 2 other fieldsHigh correlation
redTeamDragonKills is highly overall correlated with blueTeamDragonKillsHigh correlation
redTeamInhibitorsDestroyed is highly overall correlated with redTeamTowersDestroyedHigh correlation
redTeamMinionsKilled is highly overall correlated with redTeamXpHigh correlation
redTeamTotalDamageToChamps is highly overall correlated with redTeamTotalGold and 1 other fieldsHigh correlation
redTeamTotalGold is highly overall correlated with blueTeamTurretPlatesDestroyed and 4 other fieldsHigh correlation
redTeamTotalKills is highly overall correlated with redTeamTotalDamageToChamps and 2 other fieldsHigh correlation
redTeamTowersDestroyed is highly overall correlated with blueTeamTurretPlatesDestroyed and 2 other fieldsHigh correlation
redTeamTurretPlatesDestroyed is highly overall correlated with blueTeamTotalGold and 1 other fieldsHigh correlation
redTeamXp is highly overall correlated with redTeamMinionsKilled and 2 other fieldsHigh correlation
blueTeamHeraldKills is highly imbalanced (65.9%) Imbalance
blueTeamInhibitorsDestroyed is highly imbalanced (98.8%) Imbalance
redTeamHeraldKills is highly imbalanced (52.1%) Imbalance
redTeamInhibitorsDestroyed is highly imbalanced (99.0%) Imbalance
blueTeamControlWardsPlaced has 919 (3.8%) zeros Zeros
blueTeamTowersDestroyed has 13202 (54.5%) zeros Zeros
redTeamControlWardsPlaced has 940 (3.9%) zeros Zeros
redTeamTowersDestroyed has 9154 (37.8%) zeros Zeros
redTeamTurretPlatesDestroyed has 1639 (6.8%) zeros Zeros

Reproduction

Analysis started2025-03-11 12:54:28.247945
Analysis finished2025-03-11 12:55:13.350974
Duration45.1 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

blueTeamControlWardsPlaced
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6104137
Minimum0
Maximum37
Zeros919
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:13.398380image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum37
Range37
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0182638
Coefficient of variation (CV)0.55901177
Kurtosis3.5967005
Mean3.6104137
Median Absolute Deviation (MAD)1
Skewness0.7672133
Sum87437
Variance4.0733887
MonotonicityNot monotonic
2025-03-11T13:55:13.471035image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 4984
20.6%
4 4352
18.0%
2 4144
17.1%
5 3359
13.9%
1 2479
10.2%
6 1970
 
8.1%
7 1078
 
4.5%
0 919
 
3.8%
8 531
 
2.2%
9 247
 
1.0%
Other values (7) 155
 
0.6%
ValueCountFrequency (%)
0 919
 
3.8%
1 2479
10.2%
2 4144
17.1%
3 4984
20.6%
4 4352
18.0%
5 3359
13.9%
6 1970
 
8.1%
7 1078
 
4.5%
8 531
 
2.2%
9 247
 
1.0%
ValueCountFrequency (%)
37 1
 
< 0.1%
20 1
 
< 0.1%
14 3
 
< 0.1%
13 7
 
< 0.1%
12 10
 
< 0.1%
11 38
 
0.2%
10 95
 
0.4%
9 247
 
1.0%
8 531
2.2%
7 1078
4.5%

blueTeamWardsPlaced
Real number (ℝ)

Distinct364
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.364316
Minimum9
Maximum603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:13.560018image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile20
Q125
median29
Q335
95-th percentile133
Maximum603
Range594
Interquartile range (IQR)10

Descriptive statistics

Standard deviation43.477286
Coefficient of variation (CV)1.051082
Kurtosis23.728623
Mean41.364316
Median Absolute Deviation (MAD)5
Skewness4.453186
Sum1001761
Variance1890.2744
MonotonicityNot monotonic
2025-03-11T13:55:13.665000image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 1708
 
7.1%
26 1624
 
6.7%
25 1579
 
6.5%
28 1563
 
6.5%
29 1450
 
6.0%
24 1437
 
5.9%
30 1257
 
5.2%
23 1170
 
4.8%
31 1007
 
4.2%
22 908
 
3.7%
Other values (354) 10515
43.4%
ValueCountFrequency (%)
9 1
 
< 0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
12 3
 
< 0.1%
13 10
 
< 0.1%
14 19
 
0.1%
15 46
 
0.2%
16 56
 
0.2%
17 134
0.6%
18 214
0.9%
ValueCountFrequency (%)
603 1
< 0.1%
528 1
< 0.1%
483 2
< 0.1%
475 1
< 0.1%
454 1
< 0.1%
435 1
< 0.1%
434 1
< 0.1%
425 1
< 0.1%
424 1
< 0.1%
421 1
< 0.1%

blueTeamTotalKills
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.790941
Minimum0
Maximum38
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:13.760624image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q19
median12
Q316
95-th percentile22
Maximum38
Range38
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.9091786
Coefficient of variation (CV)0.38380122
Kurtosis0.24259676
Mean12.790941
Median Absolute Deviation (MAD)3
Skewness0.45778563
Sum309771
Variance24.100034
MonotonicityNot monotonic
2025-03-11T13:55:13.851099image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
12 2008
 
8.3%
11 1987
 
8.2%
13 1901
 
7.8%
10 1881
 
7.8%
14 1757
 
7.3%
9 1713
 
7.1%
15 1626
 
6.7%
8 1415
 
5.8%
16 1399
 
5.8%
17 1191
 
4.9%
Other values (28) 7340
30.3%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 35
 
0.1%
2 94
 
0.4%
3 172
 
0.7%
4 371
 
1.5%
5 582
 
2.4%
6 884
3.7%
7 1163
4.8%
8 1415
5.8%
9 1713
7.1%
ValueCountFrequency (%)
38 1
 
< 0.1%
37 1
 
< 0.1%
35 2
 
< 0.1%
34 3
 
< 0.1%
33 8
 
< 0.1%
32 5
 
< 0.1%
31 11
 
< 0.1%
30 16
 
0.1%
29 21
0.1%
28 45
0.2%

blueTeamDragonKills
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
10320 
1
9926 
2
3972 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24218
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 10320
42.6%
1 9926
41.0%
2 3972
 
16.4%

Length

2025-03-11T13:55:13.940444image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:55:13.989878image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10320
42.6%
1 9926
41.0%
2 3972
 
16.4%

Most occurring characters

ValueCountFrequency (%)
0 10320
42.6%
1 9926
41.0%
2 3972
 
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10320
42.6%
1 9926
41.0%
2 3972
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10320
42.6%
1 9926
41.0%
2 3972
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10320
42.6%
1 9926
41.0%
2 3972
 
16.4%

blueTeamHeraldKills
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
21225 
1
2992 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24218
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 21225
87.6%
1 2992
 
12.4%
2 1
 
< 0.1%

Length

2025-03-11T13:55:14.053882image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:55:14.102294image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21225
87.6%
1 2992
 
12.4%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 21225
87.6%
1 2992
 
12.4%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21225
87.6%
1 2992
 
12.4%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21225
87.6%
1 2992
 
12.4%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21225
87.6%
1 2992
 
12.4%
2 1
 
< 0.1%

blueTeamTowersDestroyed
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65480221
Minimum0
Maximum10
Zeros13202
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:14.155892image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88576457
Coefficient of variation (CV)1.3527208
Kurtosis5.310122
Mean0.65480221
Median Absolute Deviation (MAD)0
Skewness1.7734813
Sum15858
Variance0.78457887
MonotonicityNot monotonic
2025-03-11T13:55:14.223469image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 13202
54.5%
1 7493
30.9%
2 2572
 
10.6%
3 713
 
2.9%
4 170
 
0.7%
5 34
 
0.1%
6 16
 
0.1%
7 11
 
< 0.1%
8 5
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 13202
54.5%
1 7493
30.9%
2 2572
 
10.6%
3 713
 
2.9%
4 170
 
0.7%
5 34
 
0.1%
6 16
 
0.1%
7 11
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
8 5
 
< 0.1%
7 11
 
< 0.1%
6 16
 
0.1%
5 34
 
0.1%
4 170
 
0.7%
3 713
 
2.9%
2 2572
 
10.6%
1 7493
30.9%

blueTeamInhibitorsDestroyed
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
24180 
1
 
27
2
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24218
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24180
99.8%
1 27
 
0.1%
2 11
 
< 0.1%

Length

2025-03-11T13:55:14.298070image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:55:14.346495image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
0 24180
99.8%
1 27
 
0.1%
2 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 24180
99.8%
1 27
 
0.1%
2 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24180
99.8%
1 27
 
0.1%
2 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24180
99.8%
1 27
 
0.1%
2 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24180
99.8%
1 27
 
0.1%
2 11
 
< 0.1%

blueTeamTurretPlatesDestroyed
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9152696
Minimum0
Maximum22
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:14.403708image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q17
median9
Q311
95-th percentile14
Maximum22
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0839792
Coefficient of variation (CV)0.34592102
Kurtosis-0.17057086
Mean8.9152696
Median Absolute Deviation (MAD)2
Skewness0.27488658
Sum215910
Variance9.5109276
MonotonicityNot monotonic
2025-03-11T13:55:14.482166image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 3052
12.6%
9 2990
12.3%
7 2859
11.8%
10 2697
11.1%
11 2329
9.6%
6 2264
9.3%
12 1695
7.0%
5 1584
6.5%
13 1222
5.0%
4 959
 
4.0%
Other values (13) 2567
10.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 41
 
0.2%
2 156
 
0.6%
3 464
 
1.9%
4 959
 
4.0%
5 1584
6.5%
6 2264
9.3%
7 2859
11.8%
8 3052
12.6%
9 2990
12.3%
ValueCountFrequency (%)
22 1
 
< 0.1%
21 1
 
< 0.1%
20 3
 
< 0.1%
19 21
 
0.1%
18 50
 
0.2%
17 168
 
0.7%
16 296
 
1.2%
15 567
2.3%
14 798
3.3%
13 1222
5.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
1
12204 
0
12014 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24218
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 12204
50.4%
0 12014
49.6%

Length

2025-03-11T13:55:14.559338image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:55:14.605159image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
1 12204
50.4%
0 12014
49.6%

Most occurring characters

ValueCountFrequency (%)
1 12204
50.4%
0 12014
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12204
50.4%
0 12014
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12204
50.4%
0 12014
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12204
50.4%
0 12014
49.6%

blueTeamMinionsKilled
Real number (ℝ)

High correlation 

Distinct227
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean349.24189
Minimum194
Maximum465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:14.676860image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum194
5-th percentile296
Q1329
median350
Q3371
95-th percentile399
Maximum465
Range271
Interquartile range (IQR)42

Descriptive statistics

Standard deviation31.343136
Coefficient of variation (CV)0.08974621
Kurtosis0.084900614
Mean349.24189
Median Absolute Deviation (MAD)21
Skewness-0.17803199
Sum8457940
Variance982.39216
MonotonicityNot monotonic
2025-03-11T13:55:14.784898image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
347 334
 
1.4%
356 328
 
1.4%
349 326
 
1.3%
340 325
 
1.3%
345 322
 
1.3%
351 317
 
1.3%
355 313
 
1.3%
360 312
 
1.3%
357 312
 
1.3%
353 307
 
1.3%
Other values (217) 21022
86.8%
ValueCountFrequency (%)
194 1
< 0.1%
206 1
< 0.1%
219 1
< 0.1%
221 1
< 0.1%
222 1
< 0.1%
223 1
< 0.1%
225 1
< 0.1%
226 1
< 0.1%
229 1
< 0.1%
231 2
< 0.1%
ValueCountFrequency (%)
465 1
 
< 0.1%
456 1
 
< 0.1%
454 1
 
< 0.1%
452 1
 
< 0.1%
451 2
< 0.1%
450 1
 
< 0.1%
447 1
 
< 0.1%
446 1
 
< 0.1%
445 1
 
< 0.1%
444 4
< 0.1%

blueTeamJungleMinions
Real number (ℝ)

Distinct125
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.013585
Minimum0
Maximum156
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:14.886483image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q178
median88
Q397
95-th percentile112
Maximum156
Range156
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.025282
Coefficient of variation (CV)0.1593536
Kurtosis0.56398169
Mean88.013585
Median Absolute Deviation (MAD)9
Skewness0.13781886
Sum2131513
Variance196.70853
MonotonicityNot monotonic
2025-03-11T13:55:14.998628image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 1140
 
4.7%
88 1089
 
4.5%
92 1042
 
4.3%
80 971
 
4.0%
90 955
 
3.9%
86 942
 
3.9%
82 916
 
3.8%
96 880
 
3.6%
78 820
 
3.4%
94 810
 
3.3%
Other values (115) 14653
60.5%
ValueCountFrequency (%)
0 1
< 0.1%
4 2
< 0.1%
10 1
< 0.1%
15 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
24 2
< 0.1%
27 2
< 0.1%
30 2
< 0.1%
35 1
< 0.1%
ValueCountFrequency (%)
156 1
 
< 0.1%
153 3
< 0.1%
152 1
 
< 0.1%
151 1
 
< 0.1%
148 2
< 0.1%
147 1
 
< 0.1%
144 3
< 0.1%
143 1
 
< 0.1%
142 2
< 0.1%
141 1
 
< 0.1%

blueTeamTotalGold
Real number (ℝ)

High correlation 

Distinct9720
Distinct (%)40.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27830.851
Minimum17719
Maximum40968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:15.102411image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum17719
5-th percentile23611
Q125911.25
median27673.5
Q329573
95-th percentile32600.15
Maximum40968
Range23249
Interquartile range (IQR)3661.75

Descriptive statistics

Standard deviation2740.4208
Coefficient of variation (CV)0.098467014
Kurtosis0.21710354
Mean27830.851
Median Absolute Deviation (MAD)1821.5
Skewness0.34737029
Sum6.7400755 × 108
Variance7509906.1
MonotonicityNot monotonic
2025-03-11T13:55:15.204839image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27083 11
 
< 0.1%
28263 11
 
< 0.1%
27236 11
 
< 0.1%
28615 11
 
< 0.1%
27870 11
 
< 0.1%
29927 10
 
< 0.1%
26997 10
 
< 0.1%
28076 10
 
< 0.1%
25846 10
 
< 0.1%
28244 10
 
< 0.1%
Other values (9710) 24113
99.6%
ValueCountFrequency (%)
17719 1
< 0.1%
19216 1
< 0.1%
19448 1
< 0.1%
19691 1
< 0.1%
19828 1
< 0.1%
19858 1
< 0.1%
19907 1
< 0.1%
19913 1
< 0.1%
19974 1
< 0.1%
19987 1
< 0.1%
ValueCountFrequency (%)
40968 1
< 0.1%
40858 1
< 0.1%
40138 1
< 0.1%
40121 1
< 0.1%
39976 1
< 0.1%
39772 1
< 0.1%
38960 1
< 0.1%
38907 1
< 0.1%
38896 1
< 0.1%
38690 1
< 0.1%

blueTeamXp
Real number (ℝ)

High correlation 

Distinct7550
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29572.506
Minimum19061
Maximum36801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:15.300541image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum19061
5-th percentile26485.7
Q128341
median29584
Q330835.75
95-th percentile32648.15
Maximum36801
Range17740
Interquartile range (IQR)2494.75

Descriptive statistics

Standard deviation1879.4196
Coefficient of variation (CV)0.063552935
Kurtosis0.26102319
Mean29572.506
Median Absolute Deviation (MAD)1248
Skewness-0.088541218
Sum7.1618696 × 108
Variance3532217.9
MonotonicityNot monotonic
2025-03-11T13:55:15.408157image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29274 14
 
0.1%
28348 13
 
0.1%
30135 13
 
0.1%
29798 12
 
< 0.1%
29362 12
 
< 0.1%
30123 12
 
< 0.1%
28380 12
 
< 0.1%
30247 12
 
< 0.1%
30031 12
 
< 0.1%
29253 12
 
< 0.1%
Other values (7540) 24094
99.5%
ValueCountFrequency (%)
19061 1
< 0.1%
19502 1
< 0.1%
20342 1
< 0.1%
20976 1
< 0.1%
21696 1
< 0.1%
21708 1
< 0.1%
21956 1
< 0.1%
22075 1
< 0.1%
22140 1
< 0.1%
22156 1
< 0.1%
ValueCountFrequency (%)
36801 1
< 0.1%
36693 1
< 0.1%
36600 1
< 0.1%
36559 1
< 0.1%
36473 1
< 0.1%
36292 1
< 0.1%
36122 1
< 0.1%
35913 1
< 0.1%
35912 1
< 0.1%
35828 1
< 0.1%

blueTeamTotalDamageToChamps
Real number (ℝ)

High correlation 

Distinct14980
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32171.836
Minimum11022
Maximum62857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:15.512866image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum11022
5-th percentile22483.25
Q127933
median31943
Q336095.75
95-th percentile42707.15
Maximum62857
Range51835
Interquartile range (IQR)8162.75

Descriptive statistics

Standard deviation6130.7789
Coefficient of variation (CV)0.19056353
Kurtosis0.17572148
Mean32171.836
Median Absolute Deviation (MAD)4077
Skewness0.26975694
Sum7.7913753 × 108
Variance37586449
MonotonicityNot monotonic
2025-03-11T13:55:15.624511image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30749 8
 
< 0.1%
29103 7
 
< 0.1%
27748 7
 
< 0.1%
29363 7
 
< 0.1%
29089 6
 
< 0.1%
31111 6
 
< 0.1%
31022 6
 
< 0.1%
31679 6
 
< 0.1%
32533 6
 
< 0.1%
29999 6
 
< 0.1%
Other values (14970) 24153
99.7%
ValueCountFrequency (%)
11022 1
< 0.1%
11417 1
< 0.1%
12346 1
< 0.1%
12760 1
< 0.1%
13155 1
< 0.1%
13581 1
< 0.1%
13928 1
< 0.1%
14059 1
< 0.1%
14213 1
< 0.1%
14275 1
< 0.1%
ValueCountFrequency (%)
62857 1
< 0.1%
61495 1
< 0.1%
58993 1
< 0.1%
58666 1
< 0.1%
58646 1
< 0.1%
58167 1
< 0.1%
57781 1
< 0.1%
56827 1
< 0.1%
56283 1
< 0.1%
56213 1
< 0.1%

redTeamControlWardsPlaced
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6433231
Minimum0
Maximum15
Zeros940
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:15.706529image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0265715
Coefficient of variation (CV)0.55624259
Kurtosis0.26983681
Mean3.6433231
Median Absolute Deviation (MAD)1
Skewness0.528118
Sum88234
Variance4.106992
MonotonicityNot monotonic
2025-03-11T13:55:15.778496image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 4811
19.9%
4 4368
18.0%
2 4057
16.8%
5 3310
13.7%
1 2491
10.3%
6 2090
8.6%
7 1189
 
4.9%
0 940
 
3.9%
8 581
 
2.4%
9 240
 
1.0%
Other values (6) 141
 
0.6%
ValueCountFrequency (%)
0 940
 
3.9%
1 2491
10.3%
2 4057
16.8%
3 4811
19.9%
4 4368
18.0%
5 3310
13.7%
6 2090
8.6%
7 1189
 
4.9%
8 581
 
2.4%
9 240
 
1.0%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 14
 
0.1%
11 43
 
0.2%
10 78
 
0.3%
9 240
 
1.0%
8 581
 
2.4%
7 1189
4.9%
6 2090
8.6%

redTeamWardsPlaced
Real number (ℝ)

Distinct396
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.412792
Minimum9
Maximum576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:15.872258image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile21
Q125
median29
Q337
95-th percentile132
Maximum576
Range567
Interquartile range (IQR)12

Descriptive statistics

Standard deviation46.961504
Coefficient of variation (CV)1.0817435
Kurtosis25.452281
Mean43.412792
Median Absolute Deviation (MAD)5
Skewness4.5474842
Sum1051371
Variance2205.3828
MonotonicityNot monotonic
2025-03-11T13:55:15.978680image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 1572
 
6.5%
26 1566
 
6.5%
28 1557
 
6.4%
25 1446
 
6.0%
29 1415
 
5.8%
24 1308
 
5.4%
30 1239
 
5.1%
23 1123
 
4.6%
31 1098
 
4.5%
22 884
 
3.7%
Other values (386) 11010
45.5%
ValueCountFrequency (%)
9 2
 
< 0.1%
11 4
 
< 0.1%
12 3
 
< 0.1%
13 12
 
< 0.1%
14 14
 
0.1%
15 35
 
0.1%
16 68
 
0.3%
17 119
0.5%
18 188
0.8%
19 287
1.2%
ValueCountFrequency (%)
576 1
< 0.1%
551 1
< 0.1%
526 2
< 0.1%
525 1
< 0.1%
511 1
< 0.1%
506 1
< 0.1%
496 1
< 0.1%
493 1
< 0.1%
486 1
< 0.1%
470 1
< 0.1%

redTeamTotalKills
Real number (ℝ)

High correlation 

Distinct37
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.861673
Minimum0
Maximum37
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:16.080806image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q19
median13
Q316
95-th percentile21
Maximum37
Range37
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.8490473
Coefficient of variation (CV)0.37701528
Kurtosis0.18949388
Mean12.861673
Median Absolute Deviation (MAD)3
Skewness0.42670228
Sum311484
Variance23.513259
MonotonicityNot monotonic
2025-03-11T13:55:16.173437image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
11 2054
 
8.5%
12 1947
 
8.0%
13 1940
 
8.0%
14 1862
 
7.7%
10 1854
 
7.7%
9 1692
 
7.0%
15 1580
 
6.5%
16 1456
 
6.0%
8 1383
 
5.7%
17 1226
 
5.1%
Other values (27) 7224
29.8%
ValueCountFrequency (%)
0 8
 
< 0.1%
1 27
 
0.1%
2 71
 
0.3%
3 174
 
0.7%
4 355
 
1.5%
5 564
 
2.3%
6 831
3.4%
7 1120
4.6%
8 1383
5.7%
9 1692
7.0%
ValueCountFrequency (%)
37 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 4
 
< 0.1%
32 5
 
< 0.1%
31 13
 
0.1%
30 19
 
0.1%
29 24
 
0.1%
28 46
0.2%
27 67
0.3%

redTeamDragonKills
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
1
10468 
0
8018 
2
5732 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24218
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10468
43.2%
0 8018
33.1%
2 5732
23.7%

Length

2025-03-11T13:55:16.257040image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:55:16.306284image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
1 10468
43.2%
0 8018
33.1%
2 5732
23.7%

Most occurring characters

ValueCountFrequency (%)
1 10468
43.2%
0 8018
33.1%
2 5732
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10468
43.2%
0 8018
33.1%
2 5732
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10468
43.2%
0 8018
33.1%
2 5732
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10468
43.2%
0 8018
33.1%
2 5732
23.7%

redTeamHeraldKills
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
21720 
1
2498 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24218
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21720
89.7%
1 2498
 
10.3%

Length

2025-03-11T13:55:16.371240image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:55:16.416357image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
0 21720
89.7%
1 2498
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 21720
89.7%
1 2498
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21720
89.7%
1 2498
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21720
89.7%
1 2498
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21720
89.7%
1 2498
 
10.3%

redTeamTowersDestroyed
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95507474
Minimum0
Maximum10
Zeros9154
Zeros (%)37.8%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:16.465001image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.98183361
Coefficient of variation (CV)1.0280176
Kurtosis2.8052214
Mean0.95507474
Median Absolute Deviation (MAD)1
Skewness1.2443994
Sum23130
Variance0.96399725
MonotonicityNot monotonic
2025-03-11T13:55:16.529375image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 9234
38.1%
0 9154
37.8%
2 4151
17.1%
3 1301
 
5.4%
4 270
 
1.1%
5 67
 
0.3%
6 21
 
0.1%
7 13
 
0.1%
8 5
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 9154
37.8%
1 9234
38.1%
2 4151
17.1%
3 1301
 
5.4%
4 270
 
1.1%
5 67
 
0.3%
6 21
 
0.1%
7 13
 
0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
8 5
 
< 0.1%
7 13
 
0.1%
6 21
 
0.1%
5 67
 
0.3%
4 270
 
1.1%
3 1301
 
5.4%
2 4151
17.1%
1 9234
38.1%

redTeamInhibitorsDestroyed
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
24186 
1
 
27
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24218
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24186
99.9%
1 27
 
0.1%
2 5
 
< 0.1%

Length

2025-03-11T13:55:16.599715image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:55:16.647615image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
0 24186
99.9%
1 27
 
0.1%
2 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 24186
99.9%
1 27
 
0.1%
2 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24186
99.9%
1 27
 
0.1%
2 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24186
99.9%
1 27
 
0.1%
2 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24186
99.9%
1 27
 
0.1%
2 5
 
< 0.1%

redTeamTurretPlatesDestroyed
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8836403
Minimum0
Maximum15
Zeros1639
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:16.697479image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5909249
Coefficient of variation (CV)0.66713822
Kurtosis0.25503511
Mean3.8836403
Median Absolute Deviation (MAD)2
Skewness0.70969073
Sum94054
Variance6.7128917
MonotonicityNot monotonic
2025-03-11T13:55:16.771800image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 3727
15.4%
3 3700
15.3%
4 3503
14.5%
1 2911
12.0%
5 2829
11.7%
6 2055
8.5%
0 1639
6.8%
7 1496
6.2%
8 974
 
4.0%
9 623
 
2.6%
Other values (6) 761
 
3.1%
ValueCountFrequency (%)
0 1639
6.8%
1 2911
12.0%
2 3727
15.4%
3 3700
15.3%
4 3503
14.5%
5 2829
11.7%
6 2055
8.5%
7 1496
6.2%
8 974
 
4.0%
9 623
 
2.6%
ValueCountFrequency (%)
15 5
 
< 0.1%
14 10
 
< 0.1%
13 50
 
0.2%
12 101
 
0.4%
11 204
 
0.8%
10 391
 
1.6%
9 623
 
2.6%
8 974
4.0%
7 1496
6.2%
6 2055
8.5%

redTeamMinionsKilled
Real number (ℝ)

High correlation 

Distinct233
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.29036
Minimum188
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:16.861887image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum188
5-th percentile297
Q1330
median351
Q3372
95-th percentile401
Maximum464
Range276
Interquartile range (IQR)42

Descriptive statistics

Standard deviation31.595285
Coefficient of variation (CV)0.090197415
Kurtosis0.19711802
Mean350.29036
Median Absolute Deviation (MAD)21
Skewness-0.21130245
Sum8483332
Variance998.26205
MonotonicityNot monotonic
2025-03-11T13:55:16.959967image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
355 347
 
1.4%
346 333
 
1.4%
356 324
 
1.3%
349 321
 
1.3%
343 321
 
1.3%
364 321
 
1.3%
347 319
 
1.3%
357 316
 
1.3%
359 314
 
1.3%
354 313
 
1.3%
Other values (223) 20989
86.7%
ValueCountFrequency (%)
188 1
< 0.1%
193 1
< 0.1%
200 1
< 0.1%
206 1
< 0.1%
213 1
< 0.1%
216 1
< 0.1%
217 1
< 0.1%
221 1
< 0.1%
222 1
< 0.1%
223 2
< 0.1%
ValueCountFrequency (%)
464 1
 
< 0.1%
463 1
 
< 0.1%
458 1
 
< 0.1%
449 1
 
< 0.1%
448 4
< 0.1%
447 1
 
< 0.1%
446 2
< 0.1%
445 3
< 0.1%
444 2
< 0.1%
443 3
< 0.1%

redTeamJungleMinions
Real number (ℝ)

Distinct125
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.547444
Minimum0
Maximum156
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:17.057017image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q179
median88
Q398
95-th percentile112
Maximum156
Range156
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.121506
Coefficient of variation (CV)0.15947955
Kurtosis0.80705223
Mean88.547444
Median Absolute Deviation (MAD)9
Skewness0.076727619
Sum2144442
Variance199.41694
MonotonicityNot monotonic
2025-03-11T13:55:17.533631image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 1177
 
4.9%
88 1120
 
4.6%
80 1061
 
4.4%
92 1046
 
4.3%
86 948
 
3.9%
90 937
 
3.9%
96 929
 
3.8%
82 900
 
3.7%
94 849
 
3.5%
76 805
 
3.3%
Other values (115) 14446
59.6%
ValueCountFrequency (%)
0 3
< 0.1%
4 3
< 0.1%
12 2
< 0.1%
16 4
< 0.1%
20 1
 
< 0.1%
24 1
 
< 0.1%
26 1
 
< 0.1%
28 1
 
< 0.1%
30 2
< 0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
156 1
 
< 0.1%
152 1
 
< 0.1%
151 1
 
< 0.1%
148 3
< 0.1%
146 1
 
< 0.1%
145 2
< 0.1%
143 1
 
< 0.1%
142 2
< 0.1%
141 1
 
< 0.1%
140 1
 
< 0.1%

redTeamTotalGold
Real number (ℝ)

High correlation 

Distinct9556
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27787.866
Minimum18247
Maximum41227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:17.638503image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum18247
5-th percentile23655.85
Q125910
median27629
Q329512.75
95-th percentile32413.15
Maximum41227
Range22980
Interquartile range (IQR)3602.75

Descriptive statistics

Standard deviation2693.4593
Coefficient of variation (CV)0.096929333
Kurtosis0.28249003
Mean27787.866
Median Absolute Deviation (MAD)1790
Skewness0.3448414
Sum6.7296654 × 108
Variance7254723.1
MonotonicityNot monotonic
2025-03-11T13:55:17.742146image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27309 12
 
< 0.1%
26056 11
 
< 0.1%
27318 11
 
< 0.1%
25764 10
 
< 0.1%
28183 10
 
< 0.1%
27182 10
 
< 0.1%
27881 10
 
< 0.1%
27005 10
 
< 0.1%
25327 10
 
< 0.1%
28530 9
 
< 0.1%
Other values (9546) 24115
99.6%
ValueCountFrequency (%)
18247 1
< 0.1%
18313 1
< 0.1%
18614 1
< 0.1%
18791 1
< 0.1%
18825 1
< 0.1%
19150 1
< 0.1%
19428 1
< 0.1%
19467 1
< 0.1%
19472 1
< 0.1%
19512 1
< 0.1%
ValueCountFrequency (%)
41227 1
< 0.1%
40546 1
< 0.1%
40168 1
< 0.1%
39893 1
< 0.1%
39258 1
< 0.1%
39032 1
< 0.1%
38998 1
< 0.1%
38799 1
< 0.1%
38784 1
< 0.1%
38727 1
< 0.1%

redTeamXp
Real number (ℝ)

High correlation 

Distinct7574
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29618.845
Minimum17602
Maximum36797
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:17.838018image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum17602
5-th percentile26511
Q128387
median29631
Q330865.75
95-th percentile32683.45
Maximum36797
Range19195
Interquartile range (IQR)2478.75

Descriptive statistics

Standard deviation1896.2608
Coefficient of variation (CV)0.064022105
Kurtosis0.50651538
Mean29618.845
Median Absolute Deviation (MAD)1241
Skewness-0.1386563
Sum7.1730918 × 108
Variance3595804.9
MonotonicityNot monotonic
2025-03-11T13:55:17.943013image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29555 13
 
0.1%
29234 13
 
0.1%
30716 13
 
0.1%
29395 13
 
0.1%
29668 13
 
0.1%
29652 13
 
0.1%
29411 12
 
< 0.1%
30221 12
 
< 0.1%
29074 12
 
< 0.1%
29630 12
 
< 0.1%
Other values (7564) 24092
99.5%
ValueCountFrequency (%)
17602 1
< 0.1%
18965 1
< 0.1%
19411 1
< 0.1%
19986 1
< 0.1%
20295 1
< 0.1%
20552 2
< 0.1%
20735 1
< 0.1%
20830 1
< 0.1%
20883 1
< 0.1%
21057 1
< 0.1%
ValueCountFrequency (%)
36797 1
< 0.1%
36715 1
< 0.1%
36431 1
< 0.1%
36402 1
< 0.1%
36399 1
< 0.1%
36388 1
< 0.1%
36379 1
< 0.1%
36196 1
< 0.1%
36178 1
< 0.1%
36146 1
< 0.1%

redTeamTotalDamageToChamps
Real number (ℝ)

High correlation 

Distinct14812
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32154.973
Minimum10383
Maximum62452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-11T13:55:18.047309image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum10383
5-th percentile22692.1
Q128024
median31936
Q336028
95-th percentile42467.15
Maximum62452
Range52069
Interquartile range (IQR)8004

Descriptive statistics

Standard deviation6040.3295
Coefficient of variation (CV)0.18785055
Kurtosis0.18788981
Mean32154.973
Median Absolute Deviation (MAD)4007.5
Skewness0.24063867
Sum7.7872914 × 108
Variance36485580
MonotonicityNot monotonic
2025-03-11T13:55:18.160883image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30608 9
 
< 0.1%
34428 7
 
< 0.1%
31442 7
 
< 0.1%
33959 7
 
< 0.1%
37228 7
 
< 0.1%
30549 7
 
< 0.1%
31222 7
 
< 0.1%
30663 7
 
< 0.1%
29297 7
 
< 0.1%
32359 6
 
< 0.1%
Other values (14802) 24147
99.7%
ValueCountFrequency (%)
10383 1
< 0.1%
10829 1
< 0.1%
11718 1
< 0.1%
12850 1
< 0.1%
13104 1
< 0.1%
13215 1
< 0.1%
13272 1
< 0.1%
13733 1
< 0.1%
13852 1
< 0.1%
13864 1
< 0.1%
ValueCountFrequency (%)
62452 1
< 0.1%
60096 1
< 0.1%
58007 1
< 0.1%
57908 1
< 0.1%
56473 1
< 0.1%
56367 1
< 0.1%
56159 1
< 0.1%
56087 1
< 0.1%
55464 1
< 0.1%
55293 1
< 0.1%

blueWin
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
0
12241 
1
11977 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters24218
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 12241
50.5%
1 11977
49.5%

Length

2025-03-11T13:55:18.251278image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:55:18.298568image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12241
50.5%
1 11977
49.5%

Most occurring characters

ValueCountFrequency (%)
0 12241
50.5%
1 11977
49.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12241
50.5%
1 11977
49.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12241
50.5%
1 11977
49.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12241
50.5%
1 11977
49.5%

Interactions

2025-03-11T13:55:11.274631image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:32.885179image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:35.549560image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:37.839227image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:40.304349image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:44.602072image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:46.218771image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:48.183755image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:50.067478image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:51.869168image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:53.581906image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:55.238801image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:57.202509image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:58.857815image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:00.545553image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:02.474748image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:04.242095image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:05.874836image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:07.613946image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:09.196143image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:11.359001image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:33.019526image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:35.666289image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:37.954903image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:40.408041image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:44.684198image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:46.303567image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:48.284748image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:50.152416image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:51.957342image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:53.666637image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:55.326731image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:57.287008image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:58.943100image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:00.629146image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:02.562741image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:04.326523image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:05.962198image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:07.701259image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:09.582855image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:11.441658image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:33.166131image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:35.776688image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:38.064902image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:40.512093image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:44.767741image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:46.384348image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:48.378341image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:50.235368image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:52.043814image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:53.749992image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:55.413711image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:57.371198image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:59.026753image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:00.727572image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:02.647722image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:04.407867image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:06.050810image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:07.780997image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:09.670971image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:11.524137image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:33.297487image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:35.891851image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:38.164734image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:40.613834image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:44.847538image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:46.466901image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:48.467697image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:50.330058image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:52.131295image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:53.833131image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:55.499152image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:57.455278image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:59.111016image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:00.811714image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:02.751957image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:04.488112image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:06.137752image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:07.861050image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:09.767122image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:11.603098image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:33.409516image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:36.003556image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:38.265199image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:40.719597image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:44.924345image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:46.545587image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:48.551277image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:50.411190image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:52.214550image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:53.913333image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:55.583101image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
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2025-03-11T13:55:00.021770image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:01.714032image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:03.728318image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:05.375215image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:07.090894image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:08.724259image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:10.732154image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:12.499227image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:34.872702image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:37.269139image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:39.667788image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:42.026572image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:45.820585image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:47.693313image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:49.521764image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:51.284335image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:53.153274image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:54.820132image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:56.762958image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:58.444375image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:00.112271image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:01.794480image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:03.812031image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:05.458810image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:07.178831image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:08.805047image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:10.842299image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:12.577996image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:34.994248image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:37.381338image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:39.759228image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:44.142629image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:45.897831image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:47.771943image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:49.625260image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:51.363074image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:53.237479image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:54.901160image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:56.855086image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:58.524662image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:00.198940image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:01.872660image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:03.895945image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:05.534459image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:07.264036image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:08.882119image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:10.927619image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:12.661152image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:35.167611image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:37.499392image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:39.867231image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:44.304550image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:45.979780image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:47.856994image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:49.735694image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:51.458107image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:53.327969image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:54.987632image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:56.944383image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:58.610901image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:00.288488image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:01.955203image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:03.983444image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:05.624759image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:07.352818image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:08.964306image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:11.017674image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:12.738693image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:35.293333image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:37.604765image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:40.073594image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:44.431518image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:46.055725image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:47.939590image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:49.857222image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:51.701984image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:53.408284image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:55.066029image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:57.026057image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:58.689388image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:00.369941image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:02.305992image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:04.066625image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:05.702374image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:07.435906image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:09.036316image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:11.099867image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:12.822361image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:35.433846image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:37.727276image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:40.193953image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:44.520204image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:46.140048image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:48.039942image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:49.974317image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:51.787703image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:53.496131image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:55.155571image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:57.114871image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:54:58.775816image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:00.458505image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:02.393603image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:04.155792image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:05.792889image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:07.526702image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:09.118947image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-11T13:55:11.188820image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-03-11T13:55:18.377621image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
blueTeamControlWardsPlacedblueTeamDragonKillsblueTeamFirstBloodblueTeamHeraldKillsblueTeamInhibitorsDestroyedblueTeamJungleMinionsblueTeamMinionsKilledblueTeamTotalDamageToChampsblueTeamTotalGoldblueTeamTotalKillsblueTeamTowersDestroyedblueTeamTurretPlatesDestroyedblueTeamWardsPlacedblueTeamXpblueWinredTeamControlWardsPlacedredTeamDragonKillsredTeamHeraldKillsredTeamInhibitorsDestroyedredTeamJungleMinionsredTeamMinionsKilledredTeamTotalDamageToChampsredTeamTotalGoldredTeamTotalKillsredTeamTowersDestroyedredTeamTurretPlatesDestroyedredTeamWardsPlacedredTeamXp
blueTeamControlWardsPlaced1.0000.0460.0400.0210.000-0.0010.0790.0300.0720.0730.030-0.0750.2740.0480.0570.0090.0350.0320.016-0.0270.022-0.099-0.096-0.124-0.0740.0310.026-0.083
blueTeamDragonKills0.0461.0000.1370.0440.0200.1350.0900.1360.1930.1860.1190.0990.0180.1910.3190.0460.6190.0490.0110.0960.1070.1370.1980.1920.0930.1370.0000.188
blueTeamFirstBlood0.0400.1371.0000.0500.0280.0730.1140.1440.2470.2240.1400.1450.0150.1620.1730.0610.1350.0490.0140.0730.1300.1520.2530.2290.1160.1790.0000.182
blueTeamHeraldKills0.0210.0440.0501.0000.0000.1080.0600.0250.1050.0700.0730.0570.0110.1180.1080.0130.0300.1270.0000.0250.0260.0970.0990.0980.0540.0820.0000.089
blueTeamInhibitorsDestroyed0.0000.0200.0280.0001.0000.0220.0450.0840.2040.1200.5630.0210.0060.0290.0390.0270.0330.0100.0510.1720.2430.1480.2890.0790.0000.1330.0000.337
blueTeamJungleMinions-0.0010.1350.0730.1080.0221.0000.2300.0310.2380.0700.176-0.171-0.0080.4510.200-0.0300.0840.0470.062-0.207-0.004-0.271-0.285-0.281-0.1410.182-0.029-0.158
blueTeamMinionsKilled0.0790.0900.1140.0600.0450.2301.000-0.0250.270-0.0150.257-0.3340.0390.5590.2430.0220.0720.0690.1950.0140.083-0.448-0.424-0.458-0.2740.2670.026-0.135
blueTeamTotalDamageToChamps0.0300.1360.1440.0250.0840.031-0.0251.0000.6970.7600.318-0.0830.0260.3910.280-0.0980.1590.1210.058-0.284-0.4730.215-0.0720.075-0.1250.347-0.054-0.326
blueTeamTotalGold0.0720.1930.2470.1050.2040.2380.2700.6971.0000.8820.594-0.1770.0390.6690.448-0.1030.2010.1430.189-0.294-0.443-0.064-0.274-0.165-0.2000.600-0.052-0.489
blueTeamTotalKills0.0730.1860.2240.0700.1200.070-0.0150.7600.8821.0000.392-0.1070.0270.5200.384-0.1270.2040.1300.045-0.293-0.4730.078-0.171-0.066-0.1540.410-0.068-0.472
blueTeamTowersDestroyed0.0300.1190.1400.0730.5630.1760.2570.3180.5940.3921.000-0.1610.0290.3910.261-0.0800.1210.0770.133-0.181-0.325-0.196-0.313-0.227-0.2410.613-0.039-0.374
blueTeamTurretPlatesDestroyed-0.0750.0990.1450.0570.021-0.171-0.334-0.083-0.177-0.107-0.1611.000-0.044-0.2950.2580.0170.1030.1220.0930.1370.1880.3490.5630.4070.569-0.2610.0150.326
blueTeamWardsPlaced0.2740.0180.0150.0110.006-0.0080.0390.0260.0390.0270.029-0.0441.0000.0150.0050.0350.0120.0190.000-0.0360.021-0.063-0.064-0.070-0.0470.0240.079-0.074
blueTeamXp0.0480.1910.1620.1180.0290.4510.5590.3910.6690.5200.391-0.2950.0151.0000.429-0.0900.1710.1370.294-0.155-0.155-0.308-0.476-0.455-0.2980.395-0.051-0.330
blueWin0.0570.3190.1730.1080.0390.2000.2430.2800.4480.3840.2610.2580.0050.4291.0000.0520.3090.1160.0210.2010.2600.2760.4380.3770.2360.3230.0100.427
redTeamControlWardsPlaced0.0090.0460.0610.0130.027-0.0300.022-0.098-0.103-0.127-0.0800.0170.035-0.0900.0521.0000.0590.0390.0000.0010.0810.0290.0760.0740.008-0.0840.2640.051
redTeamDragonKills0.0350.6190.1350.0300.0330.0840.0720.1590.2010.2040.1210.1030.0120.1710.3090.0591.0000.0640.0110.1520.1200.1120.1900.1720.0910.1330.0000.204
redTeamHeraldKills0.0320.0490.0490.1270.0100.0470.0690.1210.1430.1300.0770.1220.0190.1370.1160.0390.0641.0000.0230.1530.0950.0540.1670.1080.1270.1040.0190.182
redTeamInhibitorsDestroyed0.0160.0110.0140.0000.0510.0620.1950.0580.1890.0450.1330.0930.0000.2940.0210.0000.0110.0231.0000.0490.1350.0450.1570.0390.6350.0440.0000.107
redTeamJungleMinions-0.0270.0960.0730.0250.172-0.2070.014-0.284-0.294-0.293-0.1810.137-0.036-0.1550.2010.0010.1520.1530.0491.0000.2360.0150.2340.0610.129-0.200-0.0070.452
redTeamMinionsKilled0.0220.1070.1300.0260.243-0.0040.083-0.473-0.443-0.473-0.3250.1880.021-0.1550.2600.0810.1200.0950.1350.2361.000-0.0220.281-0.0080.203-0.3620.0510.572
redTeamTotalDamageToChamps-0.0990.1370.1520.0970.148-0.271-0.4480.215-0.0640.078-0.1960.349-0.063-0.3080.2760.0290.1120.0540.0450.015-0.0221.0000.6920.7530.290-0.1720.0320.379
redTeamTotalGold-0.0960.1980.2530.0990.289-0.285-0.424-0.072-0.274-0.171-0.3130.563-0.064-0.4760.4380.0760.1900.1670.1570.2340.2810.6921.0000.8790.541-0.3080.0350.671
redTeamTotalKills-0.1240.1920.2290.0980.079-0.281-0.4580.075-0.165-0.066-0.2270.407-0.070-0.4550.3770.0740.1720.1080.0390.061-0.0080.7530.8791.0000.352-0.2130.0230.513
redTeamTowersDestroyed-0.0740.0930.1160.0540.000-0.141-0.274-0.125-0.200-0.154-0.2410.569-0.047-0.2980.2360.0080.0910.1270.6350.1290.2030.2900.5410.3521.000-0.1640.0080.323
redTeamTurretPlatesDestroyed0.0310.1370.1790.0820.1330.1820.2670.3470.6000.4100.613-0.2610.0240.3950.323-0.0840.1330.1040.044-0.200-0.362-0.172-0.308-0.213-0.1641.000-0.047-0.372
redTeamWardsPlaced0.0260.0000.0000.0000.000-0.0290.026-0.054-0.052-0.068-0.0390.0150.079-0.0510.0100.2640.0000.0190.000-0.0070.0510.0320.0350.0230.008-0.0471.0000.021
redTeamXp-0.0830.1880.1820.0890.337-0.158-0.135-0.326-0.489-0.472-0.3740.326-0.074-0.3300.4270.0510.2040.1820.1070.4520.5720.3790.6710.5130.323-0.3720.0211.000

Missing values

2025-03-11T13:55:12.976700image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-11T13:55:13.188007image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

blueTeamControlWardsPlacedblueTeamWardsPlacedblueTeamTotalKillsblueTeamDragonKillsblueTeamHeraldKillsblueTeamTowersDestroyedblueTeamInhibitorsDestroyedblueTeamTurretPlatesDestroyedblueTeamFirstBloodblueTeamMinionsKilledblueTeamJungleMinionsblueTeamTotalGoldblueTeamXpblueTeamTotalDamageToChampsredTeamControlWardsPlacedredTeamWardsPlacedredTeamTotalKillsredTeamDragonKillsredTeamHeraldKillsredTeamTowersDestroyedredTeamInhibitorsDestroyedredTeamTurretPlatesDestroyedredTeamMinionsKilledredTeamJungleMinionsredTeamTotalGoldredTeamXpredTeamTotalDamageToChampsblueWin
matchId
EUW1_6882489515'22342000703881062392329798218426341600201400942963732613272391
EUW1_6882416210'246121010100348982785230530358176261700104373762897030320365850
EUW1_6881092720'2191300001103319628126295013803932411810203340843051029464429840
EUW1_6879405717'3288011091355842733330466313380231010005378872515731069282290
EUW1_6879389461'33011111070370100277723106626676726710105382962605229475192451
EUW1_6879371828'22430000160369802401126872224141331521303357702836830422309670
EUW1_6878383684'322152010100331962771730575312573128900105403962858429831315631
EUW1_6878298308'030111000813921042783431205240440235000023591002410529335240141
EUW1_6878182424'2307001091403882733930398318532231110004397882647030053257911
EUW1_6878139143'23415010090314962822030249318885281610002381882880631373370850
blueTeamControlWardsPlacedblueTeamWardsPlacedblueTeamTotalKillsblueTeamDragonKillsblueTeamHeraldKillsblueTeamTowersDestroyedblueTeamInhibitorsDestroyedblueTeamTurretPlatesDestroyedblueTeamFirstBloodblueTeamMinionsKilledblueTeamJungleMinionsblueTeamTotalGoldblueTeamXpblueTeamTotalDamageToChampsredTeamControlWardsPlacedredTeamWardsPlacedredTeamTotalKillsredTeamDragonKillsredTeamHeraldKillsredTeamTowersDestroyedredTeamInhibitorsDestroyedredTeamTurretPlatesDestroyedredTeamMinionsKilledredTeamJungleMinionsredTeamTotalGoldredTeamXpredTeamTotalDamageToChampsblueWin
matchId
EUW1_6880681063'62910100070377972596929275326791241111001375822580929984296871
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